diff --git a/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-dark.png b/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-dark.png index 32e6f00..a238d36 100644 Binary files a/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-dark.png and b/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-dark.png differ diff --git a/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-light.png b/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-light.png index 7e497fa..07f60cf 100644 Binary files a/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-light.png and b/docs/source/figures/illustrations/bulk-water/effect_L_on_R1-light.png differ diff --git a/docs/source/figures/illustrations/bulk-water/experimental_comparison-dark.png b/docs/source/figures/illustrations/bulk-water/experimental_comparison-dark.png index 6588c51..340b18d 100644 Binary files a/docs/source/figures/illustrations/bulk-water/experimental_comparison-dark.png and b/docs/source/figures/illustrations/bulk-water/experimental_comparison-dark.png differ diff --git a/docs/source/figures/illustrations/bulk-water/experimental_comparison-light.png b/docs/source/figures/illustrations/bulk-water/experimental_comparison-light.png index a2c28ec..09acb5b 100644 Binary files a/docs/source/figures/illustrations/bulk-water/experimental_comparison-light.png and b/docs/source/figures/illustrations/bulk-water/experimental_comparison-light.png differ diff --git a/examples/illustrations/bulk-water-tip4p-vs-size/analysis.py b/examples/illustrations/bulk-water-tip4p-vs-size/analysis.py index 0e8fb41..e43395d 100644 --- a/examples/illustrations/bulk-water-tip4p-vs-size/analysis.py +++ b/examples/illustrations/bulk-water-tip4p-vs-size/analysis.py @@ -18,8 +18,8 @@ R1_intra = 1e-5 R1_inter = 1e-5 while run: - for N in [25, 39, 62, 99, 158, 251, 398, 631, 1002, 1589, 2521]: - for n in range(10): + for N in [25, 39, 62, 99, 158, 251, 398, 631, 1002, 1589, 2521, 6500, 10000]: + for n in range(20): # Import MDA universe datapath = path + "N"+str(N)+"/" if os.path.exists(datapath+"prod-"+str(n)+".xtc"): @@ -28,10 +28,14 @@ name_inter = "N"+str(N)+"_inter_n"+str(n) n_intra = measure_N(name_intra) n_inter = measure_N(name_inter) - u = mda.Universe(datapath+"topology.data", datapath+"prod-"+str(n)+".xtc") - hydrogen = u.select_atoms("type 2") + try: + u = mda.Universe(datapath+"topology.data", datapath+"prod-"+str(n)+".xtc") + hydrogen = u.select_atoms("type 2") + except: + u = mda.Universe(datapath+"prod.tpr", datapath+"prod-"+str(n)+".xtc") + hydrogen = u.select_atoms("type HW") n_hydrogen = hydrogen.n_atoms - if n_intra < n_hydrogen: + if n_intra < n_hydrogen*2: # Calculated NMR properties intra = nmrformd.NMR(u, atom_group = hydrogen, neighbor_group = hydrogen, @@ -44,13 +48,12 @@ type_analysis = "inter_molecular", number_i = 1) save_result(inter, name = name_inter) - else: - print("N", N, "no calculation") - #run = False - # Print information - R1_intra = measure_R1(name_intra) - R1_inter = measure_R1(name_inter) - print("N =", N, "n", n, "----", n_intra, n_hydrogen, - "R1:", np.round(R1_intra,2), np.round(R1_inter,2), - "T1:", np.round(1/(R1_intra+R1_inter),2)) + # Print information + R1_intra = measure_R1(name_intra) + R1_inter = measure_R1(name_inter) + print("N =", N, "n", n, "----", n_intra, n_hydrogen, + "R1:", np.round(R1_intra,2), np.round(R1_inter,2), + "T1:", np.round(1/(R1_intra+R1_inter),2)) + #else: + # print("N", N, "no calculation") print(" ") diff --git a/examples/illustrations/bulk-water-tip4p-vs-size/raw_data/N1002_inter_n0.npy b/examples/illustrations/bulk-water-tip4p-vs-size/raw_data/N1002_inter_n0.npy index fdf234b..c98ecdd 100644 Binary files a/examples/illustrations/bulk-water-tip4p-vs-size/raw_data/N1002_inter_n0.npy and b/examples/illustrations/bulk-water-tip4p-vs-size/raw_data/N1002_inter_n0.npy differ diff --git a/examples/illustrations/bulk-water-tip4p-vs-size/raw_data/N1002_inter_n1.npy 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a/examples/illustrations/bulk-water.ipynb +++ b/examples/illustrations/bulk-water.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 46, "id": "961dc0a5", "metadata": {}, "outputs": [], @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 47, "id": "936fd278", "metadata": {}, "outputs": [], @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 48, "id": "4d2e2b89", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 49, "id": "79e2ca71", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 50, "id": "a1028c0e", "metadata": {}, "outputs": [], @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 51, "id": "fc36cfad", "metadata": {}, "outputs": [], @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 52, "id": "a06bd83c", "metadata": {}, "outputs": [], @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 53, "id": "dd831612", "metadata": {}, "outputs": [], @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 54, "id": "3ef168be", "metadata": {}, "outputs": [], @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 55, "id": "dcfdac21", "metadata": {}, "outputs": [], @@ -205,13 +205,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 62, "id": "c22c851f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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7R48ebd2YhVFzDKe1aSEvAACYL4K/NXJxcaGTkxPVarWtbFsxju+fXelnPv0FRVGsKL459EtEcawoivWzn/mCvn+2Gb/A3DX1ej0NjiQpm83eOoy7Xq8rDMO1a/EpTXc8m6Ddbuv4+FiNRkO2batSqejg4ECHh4fa29tTuVyWYRjp/Tudjk5PTwn/AAAAgDUzKkjalEBoVLXfzs6OKpXKVlX9LWqO4SaFvAAAYL7Wq3feHdZsNtVqtXpuu4vzwn7zy19Ts+OPHfolojhWo+3rc1/+I/3Kz//UglaHaQRBoFqtln5tWZaq1epYj8nn82sXqE1zPJugXq+rVqvJMAxVKpWBCkbbtpXL5eQ4jk5PT3suItzFn1UAAADAurotSNqEWX+jqv0Sjx490pMnT7biQ4jFYlGvvfbaQra9br9PAwCA5SD4WwNRFMl13YHb79rFdD8I9dkvfe3W9p6jRFGsz37pv+qf/9xHlLG3q+3Hpmq327q4uEi/tixLe3t7t763k/Nh3ar9pj2edZeEfpK0v7+vTCYz8r6maWpvb08nJyeSpL29vaWsEQAAAMB4hoVm0g/mlCdVf6+88spK1nebm6r9EpVKRT/yIz+ib37zm8te3tzZtr12M+0BAMBm2+yr1VuiVqv1tA1MdLfUuwt+74/e1InbmGkbx25DX3r9zTmtCOOIokjtdjv9pGXy9cXFhc7Pz9P3dj6f18HBwa2/0DSbTXU6HVUqlZUEavM+nnXneV4a+u3u7t4Y+iWSNqB7e3sbf/wAAADYbK7r0s6wy6jQrL815jq3gbyt2i/xYz/2Y0tcFQAAwObgiu2K+b4vz/PkOI7a7fadne0nSW++dSLbNBXM0KrDMk29+dbpHFeF25imqfPz85Hfz+fzKpVKYwVK0vUvqrZtD7SaXJZ5H886C4Igra60bVuFQmHsx67q9QEAAAC6PX78WIZhrG312rKNCs3iONY3vvGNta/6G6faL1EqlZa1LAAAgI1C8Ldil5eX6Uyt4+PjVS9npeqtjmYtcjQNqdZsz2dBGNve3p5831cURTJNU6ZpKpPJTByOua6rOI61u7u7mIWOaV7Hs+6625au83wPAAAAYJjukOimmXWu68rzvKn34zjO2o0hGOa20Oyll17S66+/nn5vHWf9jVvtBwAAgNEI/lbI8zwFQTAy5Nj0uWGTKuWzGtLxdCJRLJULDK9etlwuN/PQ8O7q11UHbPM4nnXXbDYVBIGk67bC2368AAAA2D7dIdGo6jXXdfUbv/Eb6b99p2Hbtl599dW1D/9uC80ePXq01lV/w4LLhw8fyrIsNRqDY0GazeaylgYAALBRCP5WJIoiua6rbDY7UXu9bfbCOw9navMpSWEU6YV3HsxpRVim7upXLF69Xk//n59BAAAASLiuK8Mw5lIFNs9tDdt2d0g0qnot+cDtLIIgkOd5a/27yjgtMiuVylpX/fUHl5L09OlTPX36dEUrAgAA2EwEfyuSzNVa518clu2n3/+CDitFnbiDn+Qb11GlqFd+8oU5rgrLcFv166SSFp3r4OzsTJ1OZ6n7zGaz2t/fH/l93/d7Ln6MW2G5Ts8rAAAAFmOeM/MWOX+vPyRat+q1ZRu3Rea6Vv0NCy6XYZXz5RN+EOr3/uhNvfnWieqtjkr5rF5456F++v0vKGNbK10bAADYTAR/K9But9VqtVQul2XbvASJjG3pU698QL/6219RGE3e89M0DX3qlQ/yD+MNE0WRrq6uJF1X/V1eXk68jefPnw/cdnh4uBbn1yqCstv22d8Sx3Xd9MMIt3nw4MHU6wIAAMB6G3dm3rK3ddO2u61T9doyjVPtl1jXqr9h1X6zet/73nfrfMBVzm/8/tmVfvPLX9Nnv/Q1nbgN2aYpw5DiWAqiSIeVoj71ygf0ix//gN6xf7fe0wAAYDarvyp+B7muK8uyVCqVVr2UtfOLH/+Afu0//Bd5bV/RBAP/TMNQMZfRJz/+/gWuDovguq7iWYc7rrFqtbrqJQxot9s9XxuGkYaFhmEM3D95ffL5/OIXBwAAgJUZZ2beKrZ107a7rUv12rKNW+2XWLeqv0VV+7355pv62Mc+tpZB8Feefls/8+kvqNnx0w8+948+OXEb+tXf/op+7T/8F33xl/+hPvLie1axVAAAsIEI/pasXq8rDEPt7e2teilr6R37O/riL/9DfeyXPi9FGiv8Mw1Dpmnoi7/8j/gUHDCG7jafhmHo/v37K1wNAAAA1sG4M/OWva3btt1vHarXlmnY8/Hw4UNZlqVGY/gYDdu29fDhw57Zeat83orFol577bWFbDuXyy1ku7P4ytNv62O/9HlFUXzrNY8wiuW1fX3slz6v3//MJwj/AADAWAj+ligIAtVqNeXz+bX8x+e6+MiL79Hvf+YT+tnPfEFe27+x7adpXlf6ffGX/5H+3o++e4mrxLxMUxF3fHzc84nWe/fupRVrURQpiqK1aPO5jrpDP2k1rUix5bI7kpmRIn/6bZiZ6+0AAIClmefMvEXO3xtW3ZbsY9772gTDqh+fPn3aE+qNY5XPm23bd+b3t++fXelnPv2FsUK/RBTHUiT97Ge+oG9+9jU+8AwAAG51N/5ltSaSGVqL7B/v+5NdaLUsK/1FaZ185MX36JuffU2f+/If6d986b/qxG3IMk2ZhhTFUhhFOqoU9alXPqhPfvz9/MMXKdM0CbNusM1tVbEmnCPpo5+TOlfTbyO7c70dAADuGNd1ZRjG0quu5jkzb5Hz90bNsovjeO1m1i3DvFtk3pXnbZV+88tfU7Mz2WgT6Tr8a7R9fe7Lf6Rf+fmfWtDqAADAsoRhONF840lzH4K/JWk2m+p0OqpUKgsNJa6uJrvQWiqVVC6XF7Sa2bxjf0e/8vM/pX/+cx/Rl15/U2++dapas61yIacX3nmgV37yBWXs9QstgU0yyV8wwNicI4I7AACm8PjxYxmGsfSqq3nOzFvk/L1Rs+ziOF6rmXXLMuq5ntZded5WxQ9CffZLX7uxq9FNoijWZ7/0X/XPf+4jXAsBAGDDeZ6ner2+sO1TFrMkruvKtm05jrPqpWycjG3pZz70Pv2P//3f1Wf+4Uf1P/73f1c/86H33Yl/6EZ9w72BWQ2r8PU8bwUrAQAAQLekeuvJkycTf6BzHvsdZZL1zHNb42z7x3/8x7Wzs6NKpaKXXnppbvvaBPOu9kts+/O2Sr/3R2/qxB0+d3Fcx25DX3r9zTmtCAAAbCsq/pbAdV3Fcazd3d1VLwXYCv2BaBRFtPcc07DnqV6vT/ShhHa7LUnMKgUAAJij7uqtZVZdzXNm3iLn742q9ks8evRoaNVffyC4LYrFol577bWFbJt/5y/Gm2+dyDZNBTN8wNcyTb351ukcVwUAALYRwd+C+b4vz/PkOI4ymczC97ezszPRftZxvt9d1t+mZV4Vf8Pav0wy623Wx89b/74nXcu6Hc+y5fN5tVqt9OswDHV2dqb9/f1bH+u6rtrttqrV6iKXCAAAsBCrmqF3m/7qrWXNWpvnzLxFzt+7qdovkVT99e/rve9979j72SS2bcu2uaSzSeqtjgxjtm2YhlRrtuezIAAAsDKO40z0YSvf9yfqykCJzIJdXl7KMAxVKpWl7C+TySibzY79h+BvfQwb0NnpdOYS/iUVWt2azebSHj9Pw9pSTtoPeZ2OZxWGzfXsdDo6Ozsb+X7zPE/Pnj1TEAQ6ODhYygcZAAAA5u3x48d6/PjxqpcxoL+aLalWW/Z+kyq6R48e9fyuOM565rmtcbfdb9i+3njjjYn2BSxKKZ/VrJ83jWKpXKAiEwCATWdZ1kQ5zqTXYvl42AJ5nqcgCObW4pN2htvF9321221FUaQwDHsqsLodHx8rl8vJsiyZpinHcW59H3iel2630+koCIKB+zQaDfm+r0wmI9M0ZVmWCoXCXB4/L93PURzHCoJAnU5n4H6tVit9ngzDGHie1uV41oVt2yoWi2o0eudLdDodPX/+vKdCud1uq91uK45jVSoV5pQCAICN1V01NqzizHXdmWYfO44z1Qc+R81qW3TV321VdMOq50atZ57bmnTb3YZV/X3rW9+6dR/AMrzwzsOZ2nxKUhhFeuGdB3NaEQAA2FYEfwsSRVFaenl5eanLy8uJt/H8+fOB2w4PD2nnsSXa7bZqtdrY901aUWYymVvLgF3XvXWbhmHI9335vq84jmXbdhp0zfr4efE878aLL0ZXn5Qoinrum8/n0+BvXY5nnSQXSvrDP2mwqjKfz6tSqfDBAwAAsNFumqHnuq5+4zd+Y+gHxMZl27ZeffXVicO//mq2xCzz8KbZ77gz84atZ57bmnTb/fr3Na/xCcCsfvr9L+iwUtSJO/g72LiOKkW98pMvzHFVAABgG5EgLYjrundqZhgmVyqVVCqVFrLtBw8erPTx81KpVObSJnddjmfd7OzsyHEceZ6ndrutMAwVx7EMw5BlWcrlcnIchw8bAACAjXfbDL2kW8ssgiCQ53kT/ft1VLXfqHXOyywz8/rXM89tjbPOhw8fyrKsoR9gk64D2IcPH+rp06cjtwusQsa29KlXPqBf/e2vKIwmv15kmoY+9coHlbEZ2QIAAG7G1VwAuMNs215Y+ygAAIB1MWqG3qKq6cY1rJpN0lSVcbPud9TMvNsq9ea5rdvWKUlPnz5deqhn2zYt7zEXv/jxD+jX/sN/kdf2FU3wYXHTMFTMZfTJj79/gasDAADbguBvQarV6sSPOT4+7vml5t69e2lrvSiKFEURlTcAAAAAMIFVzdC7zahKuTiOp56HN8t+x52Z172eeW5rnHXOwjRN/YN/8A+m6roy7fxGoN879nf0xV/+h/rYL31eijRW+GcahkzT0Bd/+R/pHft8aBMAANyOgU0bwjRNQj8AAAAAmNBtM/RWZVSl3KNHj9LKP2n+65xmZt6o9cxzW7etc1ZRFOnP/uzP9ODBg4n/EPphnj7y4nv0+5/5hIr5jCzTuPG+pmmomM/o//f/+r/r7/3ou5e0QgAAsOkI/gAAAABgi7muq6urq1UvYyXGmaG3iufmpkq5pDKu27jr9INQ/8tXv6n/6Xf+N/3SF/5A/9Pv/G/6X776TflBOHK/3TPzhv1JZub1r+ev/uqv5rat/mObd7XfTfsCVuEjL75H3/zsa/p//Nx/p8NKUZJkmaYylinrrzs/HVWK+hc/99/pm599jdAPAABMhBIyAAAAANhijx8/lmEYK59ntwrjztDrD9pWsa7uSrlJ5+F9/+xKv/nlr+mzX/qaTtyGbNOUYUhxLAVRpMNKUZ965QN6T6Y2l5l5YRjqd3/3d+e2rf5jKxaLeu211ybazrhyudxCtgtM6h37O/qVn/8p/fOf+4i+9PqbevOtU9WabZULOb3wzgO98pMvKGNbt28IAACgD8HfGomiaODrZMYfAAAAAEyqu3JqlfPsVmGSGXrvfe97V76u7tdmknl4X3n6bf3Mp7+gZsdXGF3PCwv6frc8cRv6f//2V2Qr1j94r63/087sv2ceHx/PvI1E/7HZts2oC9wZGdvSz3zofateBgAA2CKkSmsk7hvq3P81AAAAAEwiqSxb9Ty7VZhkht4bb7yx8nX1G2ce3leeflsf+6XPy2v/IPQbJYpi+ZH0//3TQN+9im6877LdxfcnAAAAsCgEf2vC87yB2+r1+gpWAgAAAGAb9FeW3aX5ZpPO0PvWt7618nX1u23W3/fPrvQzn/6CoihWNOaHRmNdt//8d38WqNZZrw+a3qX3JwAAALBI9M5YAd/31W63FUWR4jhWEATqdDoD92u1Wjo+PlYul5NhGDJNU47j0P4TAAAAwK36K8tumxO3TSadodc/dmFV6+p306y/3/zy19Ts+GOHfolYUhAbyr33g/pn/9e/O/Ex9P/+ms1m59aWk/l7AAAAwOwI/lbA87yhFX4JwzDS/4+iqOe++Xye4A8AAADAjYZVlkmj58Rtk2ln6K1iXQ8fPpRlWWo0GkMfY9u2Hj58qKdPn6a3PXnyRB/80Mv67Je+dmt7z1GiONb/5w++of/n/+3/ooxt3f4AAAAAABuD4G8FKpWKKpXKqpcBAAAA3Cmu6+r/3979xkiS33We/8Sf/BdZVdnVVdXjGfwAjMyMXPLYY4Hxib7DZrnFmpPwaR+skXYBgTCydCOk5tmxi/d0oJVWQtp7MKczrBBIXq3MrU5es4sFnLC9p0YWMB6b9j8G70mcxNpDd1VXRVVlZGVFZMQ9KCInMzIyMzIzIv9Evl9SazpzMiMjq+ubGZmf+H5/hmEsFHq5rjvxJL5pHMdZymeBZGdZbBu6/mZZQ2+wm27Z+yVJjx49Ggr1suj1evrf/t1/0hM3PSzM6rHb1udee0Mf+cC7FtoOAAAAgPVC8AcAAABgKzx8+FCGYcwdermuq1dffVVBEMy9D7Zt65VXXik0/BvX7Rcrc9ffPGvoLaPrb9q/yaz+/Bv/RZZpzN3xJ0mWaeqNvz3JbZ8AAAAArAdmRgIAAAAovTh4ef3113VxcTHXNjzPWyj0k27XR1ukYzCLtI43y3prnGPc9VdG86yhN/izWdZ+Leo6WHxNQtOQLjvdHPYGAAAAwDoh+AMAAACQO9d15w7YihAHL2UOvaTxHW8vvfTS0HWLBKDratoaeml/4jX0lr1fi6pahqIFuv0kKYyk3UYtpz0CAAAAsC4Y9QkAAAAgd4uO1cxTMngp86jLcR1vURQNrWdXxrX+8lpDb162bctxnJHrm82mHjx4kOtjvfAXb+jzv/F/LbSNXhjq+bcf5rRHAAAAANYFwR8AAACAXA0GbesQsCUDoTKGXtL09e2S69mVKQDNu6vONE391E/9lHZ2djLfx3Gc1LUbbduWbef70fsf3X+Pjv7NH+qJ2557G/daTb38g8/nuFcAAAAA1gGjPgEAAIAtsazxm+s0VnNcIFTGUZfT1rdLrme3Dv8+ecl7Db0wDPXtb39bzz77bOY/aaFfUSq2pY+//H5ZpjHX/U3T0Mdf/mFV7OLXNwQAAACwXAR/AAAAwJZ4+PBh4UFP2ljNVQZs4wKhMoVe0vRuP0lqtVqlXOuviDX0pPX/2fzih9+vRrUi05gt/DMNQ81aRR/78A8VtGcAAAAAVolRnwAAAMAWWNb4zXUaqzktECrTqMtp3X6x+/fvl26tvyLW0IvVarVCtpuH5w729NlP/LR+4ld/RwqlMIqm3sc0DJmmoc9+4mf03MHm/94DAAAAGEXHHwAAALAFljF+c93GaqaFYWUcdZml2y9Wxq4/27bVbDYL+ZP32nx5++CL79Af/drPqVmvTB37aZqGmvWK/vjXf14/+u7vW9IeAgAAAFg2gj8AAACg5JY1fnOdxmqOC8PKFnpJ2bv9YmVe628bffDFd+ibn3ygf/bRD+mo1ZQkWaapimXKMm8/8t9rNfXPP/ohffOTDwj9AAAAgJJb79MXAQAAACxsGeM3122s5rgwLIqiUo26TPu5Hx8fy7Istdvt1PvYtq3j42M9evSof12Zxp5uo+cO9vQv/sk/0K989IP63Gtv6I2/PdFlp6vdRk3Pv/1QL//g86rY1vQNAQAAANh4BH8AAABACtd1ZRjGXEGI67ryPG/ux3YcR61Wa+77J/dl3PjNPIOetKBN0koCtmmjL1966SW99tpr/f+3yaFXWpflo0ePhkK9LDY9AMWtim3pIx9416p3AwAAAMAKEfyVzNOnT2UYo2s7NJtN7ezsrGCPAAAANtPDhw9lGMbMQYjrunr11VcVBMHcj23btl555ZVcwr9p4zfzCHrGBW1RFK0kYJs2+vL+/ful6Pqb1mU5q00OQAEAAABg011dXaVObomiaKbtsMZfyURRpDAMR/7M+osBAACwzeJAZZ713zzPWyj0k6QgCBbqGIxlGb+Zx/p244K2VawlN63bT5JarVYp1vobF+rOi7X+AAAAAGB18sp3CP5KxjAMmaY58ietCxAAAADp4kBl04OQtEAu7yBuUtC2ioBtWrdfbBWhZJ7y7vaLbWIACgAAAABlkFe+w6jPkrl7966q1eqqdwMAAGBjJQOVTR1/uOj4TT/o6Q/+4g298bdPdHV9o516Vc+//Uj/ww89r4r9VmC2TmM1s3T7xeJQclPX+ms2m3rw4EEh267VaoVsFwAAAAAw3s7OTuqSbTc3Nzo9Pc28HYI/AAAAFMZ1XRmGsRFBSiwZZG3q+m/jArkoiiYGcd85vdBv/eGf65Of+3M9cduyTVOGIUWRFIShjlpNffzl9+sXP/x+Ne0o81jNZQRsWbv9YrOGko7jyLbthddvdBxn7vsPbse2+TgHAAAAABjGJ0UAAAAU5uHDhzIMY2NCs3HjEzepE0ya3vk2Loh7/W9O9JH/9VPq3PjqhbdrCARhOLSdJ25b//L3vqh//Zk/1f/8D9+Zeaxm0V1/ac/5+PhYlmWlLo4u3YZnx8fHevToUf+6Sf/WrVZLr7zyykLrLzqOo1arNff9AQAAAACYhOAPAAAAhRgMYjYlNEt2jMU2retvnvGb/8fv/YE+8Z++qTCMFE5ZOLwXRvK6vn71P35D//QHbH3v3u3S4ascq5n2b/fo0aOhUC+Laf/WrVaL4A4AAAAAsLbMVe8AAAAAFue6ri4uLla9G0PiICYOUtbduG6/2Ouvv752P+M0Wda5i4O42OVNpP/lP349U+gXC6NIUSR9+tuBLm+iTGM1LeuttQHz/L2Y9m83q035twYAAAAAIIngDwAAoAQePny4VuFaMojZhCAlrUuuqKCqSFnXuRsM4r78pCc/VObQLxZJ8sPb+w+O1Uz7E4/VHJTX78W4Ts15bcq/NQAAAAAASYz6BAAA2HDrOFIzGcSs+6jMcV1yURQVOp4yb1m6/WJx19+f/flf6LXHoWaL/N4SSfry41Bf+epf5j5WM4u8u/1i6/5vDQAAAABAGoI/AACADTcYsq1DuDYuiFnnIGVcl1wURSNr4a3Dz3icrN1+sfv37+vf/clr8oLFHrcdSP/FjfT8vjHzfRf9vWg2m3rw4MFc952mVqsVsl0AAAAAAIpC8AcAALAiruvK87y57+84jiSNjNRcdbg2buziuoZm07rkXnrppY3o+kt7HoPjN9PYti1r70im/qvCBR7bkHRyHen5Oe676O+FbduybT7WAAAAAAAgEfwBAIAt5LquDMNYaXDjuq5effVVBcH8rVa2betd73rXWo3UnDZ2cR1Ds2ldcvfv39+Irr+0wPXRo0dTx2/+f/81uE3u5p31KckwpG5v/g2s4+8FAAAAAACbiOAPAABsnYcPH8owjJUGN57nLRT6SVIQBPr6178+cv0qQ5S0EE3S2oZmWdbEi9fCW+euv0XWuataxkKhnyQZhqH//kM/qgcf+W/m3gZjNQEAAAAAWJy56h0AAABYpjggef3113VxcbHq3VlYGI4OaIzDtWUbF6K99NJLQ9et088+65p49+/f74eY0up+xuOMG6+axWHdWGjMpyT1wkjvfsdzajabc/9hXCcAAAAAAIsj+AMAALlzXXdtgp2kOCBZt+Amb6sI18aFaOsammXp9ovFXX+D1iXAXKTbT5Le2TLkLJi53Ws19fIPzrPCHwAAAAAAyBOn1a6A7/vyfV9BECiKIhmGIdM0ValUGHEEACiFdRilmSYZkKzbuMZFrHqk5rQQbR1HZWbt9otlXevPcRzZtr3w+o2O42S6bbPZ1IMHD+Z+LEkK/s//R7/xmT9VL5x95qdpGvr4yz+sim1NvzEAAAAAACgUwd8SXV1d6erqSlE0+QsVx3EYdwQA2FiDAdCqg52kZNCzbuvNLeJ973ufoihaWbg2LUTLGpotS1pQeXx8LMuy1G63U+9j27aOj4/16NGj/nVpP+NWq6VXXnlFnufNvX+O46jVamW6rW3bCx83vvKTP6L//Q/+XF7XVzjlWHWQaRhq1ir62Id/aKHHBwAAAAAA+SBZWoIgCPT06dPM6654nifP83Tnzh01Go2C9w4AgHwNBkDrFKq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"text/plain": [ "
" ] @@ -250,7 +250,7 @@ " markersize = 12, linewidth=4, label=r'TIP4P$-\\epsilon$')\n", " complete_panel(ax[-1], r'$T$ (K)', r'$T_1$ (s)',\n", " legend=True, axis_color=mygray, xpad=15)\n", - " set_boundaries(plt, x_boundaries=(262, 332), y_boundaries=(0, 14))\n", + " set_boundaries(plt, x_boundaries=(262, 332), y_boundaries=(0, 14), y_ticks=np.arange(0, 15.5, 3))\n", " # x_ticks=np.arange(-1, 0.2, 0.2)\n", " # add_subplotlabels(fig, ax, [\"a\", \"b\"], color=mygray)\n", " save_figure(plt, fig, mode, git_path, path_figures, filename)" @@ -478,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 84, "id": "ecda8030", "metadata": {}, "outputs": [], @@ -492,14 +492,20 @@ "R1_inter_vs_N = []\n", "R2_inter_vs_N = []\n", "gij_inter_vs_N = []\n", - "for N in [\"N25\", \"N39\", \"N62\", \"N99\", \"N158\", \"N251\", \"N398\", \"N631\", \"N1002\", \"N1589\", \"N2521\", \"N4000\"]:\n", - " for n in range(10):\n", + "for N in [\"N25\", \"N39\", \"N62\", \"N99\", \"N158\", \"N251\", \"N398\", \"N631\", \"N1002\", \"N1589\", \"N2521\", \"N4000\", \"N6500\"]:\n", + " for n in range(20):\n", " if os.path.exists(\"bulk-water-tip4p-vs-size/raw_data/\"+N+\"_intra_n\"+str(n)+\".npy\"):\n", " t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-size/raw_data/\"+N+\"_intra_n\"+str(n)+\".npy\")\n", " t_vs_N, f_vs_N, gij_intra_vs_N, R1_intra_vs_N, R2_intra_vs_N = append_data(t, f, gij, R1, R2, t_vs_N, f_vs_N, gij_intra_vs_N, R1_intra_vs_N, R2_intra_vs_N)\n", " t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-size/raw_data/\"+N+\"_inter_n\"+str(n)+\".npy\")\n", " _, _, gij_inter_vs_N, R1_inter_vs_N, R2_inter_vs_N = append_data(t, f, gij, R1, R2, [], [], gij_inter_vs_N, R1_inter_vs_N, R2_inter_vs_N)\n", " all_N.append(np.int32(N[1:]))\n", + " if N == \"N4000\":\n", + " t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-temperature/raw_data/N4000_intra_T300K.npy\")\n", + " t_vs_N, f_vs_N, gij_intra_vs_N, R1_intra_vs_N, R2_intra_vs_N = append_data(t, f, gij, R1, R2, t_vs_N, f_vs_N, gij_intra_vs_N, R1_intra_vs_N, R2_intra_vs_N)\n", + " t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-temperature/raw_data/N4000_inter_T300K.npy\")\n", + " t_vs_N, f_vs_N, gij_inter_vs_N, R1_inter_vs_N, R2_inter_vs_N = append_data(t, f, gij, R1, R2, [], [], gij_inter_vs_N, R1_inter_vs_N, R2_inter_vs_N)\n", + " all_N.append(np.int32(N[1:]))\n", "all_N = np.array(all_N)\n", "R10_inter_vs_N = extract_R0(R1_inter_vs_N)\n", "R20_inter_vs_N = extract_R0(R2_inter_vs_N)\n", @@ -509,29 +515,33 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 85, + "id": "2332d2dd", + "metadata": {}, + "outputs": [], + "source": [ + "unique_N = []\n", + "unique_mR1 = []\n", + "unique_sR1 = []\n", + "for N in np.unique(all_N):\n", + " unique_N.append(N)\n", + " sub_R1 = R10_inter_vs_N[all_N == N]\n", + " unique_mR1.append(np.mean(sub_R1))\n", + " unique_sR1.append(np.std(sub_R1) / np.sqrt(len(sub_R1)))\n", + "unique_N = np.array(unique_N)\n", + "unique_mR1 = np.array(unique_mR1)\n", + "unique_sR1 = np.array(unique_sR1)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, "id": "9d58bbd2", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/simon/Git/NMR/nmrformd/examples/shared/pyplot-perso/functions.py:133: UserWarning: AutoMinorLocator does not work with logarithmic scale\n", - " fig.tight_layout()\n", - "/home/simon/Git/NMR/nmrformd/examples/shared/pyplot-perso/functions.py:133: UserWarning: AutoMinorLocator does not work with logarithmic scale\n", - " fig.tight_layout()\n", - "/home/simon/Git/NMR/nmrformd/examples/shared/pyplot-perso/functions.py:137: UserWarning: AutoMinorLocator does not work with logarithmic scale\n", - " plt.savefig(git_root + path_figures + filename + \"-light.png\",\n", - "/home/simon/Git/NMR/nmrformd/examples/shared/pyplot-perso/functions.py:142: UserWarning: AutoMinorLocator does not work with logarithmic scale\n", - " plt.savefig(git_root + path_figures + filename + \"-dark.png\",\n", - "/home/simon/.local/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: AutoMinorLocator does not work with logarithmic scale\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, { "data": { - "image/png": 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GMRgMYjQazbXMZtXr9Wg2mw8+l2q1Ohf8DQaDpYO/yblP5nOXSYCZz+efJSTM5/PT79E6X7ckSaYNx3q9vvfLmgIAAAAAAGy7jVXxisVivHv3LrrdbrRarWufHw6Hc424WblcLvL5fKRpemuwd5tyuRxHR0cbC55qtVqcn59Pb3e73aXabL1eb/pxtVq9M9RMkiQ+fvw4PddyuRxv3rxZa76rfr0m8vl8VKvV6Ha7cXBwsHIrc/I9LhQKcXh4uNYcAAAAAAAA2JzNrcH5K5PlPbvdbnQ6naWCqSzLVt5Pr1qtRqPReJSm2eHh4bSpOB6PYzAY3NvK63Q6c4+/S7vdnvu6DIfD6Ha797YEb5Km6dztVYLAer0e/X4/Wq3WSsFjp9OJ4XAYuVzuQUuFAg/3vteP1jC5/8BbNMvFOKnZoxMAAAAAYB9sPPiLuGqTNRqNaDQa0ev1ot/vx2AwWLudNlEqlaJarW5838BFjUYjut3uNIxstVp3Blzdbne6zGez2bx3brNLgt51331u+pp2u92ll+6sVCqRy+ViOBzGxcVFHB0d3fuYi4uLuLy8jEKhEK9fvxb6wTN63+vHb/3BD2KU3nxt/eb4Z/HPj/7j+HdL/3T8qPD1G48p5XPx5Xe+EP4BAAAAAOyBx0vPfqVWq0WtVouImO7VNxgMYjwe37m0Zy6Xm9sTsFKpPGnI9Pbt23j//v20jfjx48c4Pj6+1jDsdDrTduDBwcFSrb1arTZ9zOx9d5l87SZfsyRJblw6tdvtTvclLBQKkc/np1+/2+bS7Xbj8vJy2jqsVCpRKBQiIuaeq9frRZZlG9tPEXiY1jC5NfSLLIu/Nvqb8eeyT/HXRn8z/nr+1yJyuWuHjdIsWsMkTu6+BAEAAACP7f2PIv7wdyN+43ciTr753LMBYEc9evA3axLiLe6Zl6bpdMnKfD6/FS2yfD4fJycn0Wq1ot/vT8O/QqEQpVIp0jSN0Wg0DS5fvXp1b3g30Wg0Ik3T6Ha7kc/n4/Dw8N4lSweDwbWw8Dbj8Xhu6dRqtXpr8Fev16Pb7UbEVch3eXkZl5eXNx676f0UgcfzRfqz+PXsFxER8evZL+KL9Gfxg8Kff+ZZAQAAADfKsogffxnR/vnV+3ffuPEFvABwnycN/m6zLWHfonw+H8fHxzEajaZNujRNo9/vTwPAWq221t58R0dHSy2tOTFZOnXTSqVSfPbZZ9Pzm21iFgqFKBQKUalUHn15VWCDsix+c/T7MY5cFCKLceTiN0e/Hz/If90vDQAAALCN3v8w4vynVx+f//Tq9mffft45AbCTJDlLKJVKe7205eyejMDum237RUQUItP6AwAAgG2VZRE/+X5ELh+RpVfvf/L9iJNveQEvACvbvpodAOubafvNmrT+4pZ9VQEAAIBnMmn7ZVdbIUWWftX6A4AVCf4A9sik7VeI+YBvtvXHlnr/o4jf++2r9wAAALwMs22/WZPWnxfwArAiwR/Avril7Teh9bfFFjdx9z3aTcJbAABgVYttvwmtPwDWJPgD2BO3tf0mtP622E2buLNbhLcAAMCqbmv7TWj9AbAGwR/APrin7Teh9beFFn/R84vdbhLeAgAAq7qt7Teh9QfAGgR/AHvgvrbfhNbfFrKJ++4T3gIAAKu6r+034fcLAFYk+APYdUu2/Sa0/raITdz3g/AWAABY1X1tvwm/XwCwIsEfwI5btu03ofW3RWzivvuEtwAAwKqWbftN+P0CgBUI/gB21OlguHLbb2K29Xc6GD7SDLmTTdz3g/AWAABY1bJtvwm/XwCwAsEfwI7qjJKV234Ts62/zih5pBlyJ5u47z7hLQAAsKpV234Tfr8AYEnF554AAGv6VdsvjfVexZFGxG+Ofj/+QfbPbHhi3Gv2F727XuE5+cXu5FsRudVanTyBSXh7m9nw9rNvP928AACA7XXf7xG38fvFVnjf60druP4LqJvlYpzUqhucEcB1gj+AHXVYzOIku1i7up2PiJPsIi6KXi345Jb9Rc8vdltn+ktelsWf/btfRiXykYvbw9ss8jH4u1/Gzyv/SEQu55c8AAB4ySYvAo1cxIor91zJeXHoM3rf68dv/cEPYpSu/3eUUj4XX37nC78XAo9K8Aewo7J8Kf6Fym9FM7prj3Geq8e/lC9tcFbca9m234TW39aY/SXvi/H/L/6Xwz+69zG5SKPa/qP49/6f/2H8oPDn/ZIHAAAvWZrEuPt+5e06vpLFuPshCmkSUfC7/FNrDZMHhX4REaM0i9YwiZPahiYFcAPBH8CO6ozG8SF/FB/i6MHj8IRWXdZF629rTH/J+9Uyu+PILfUL+zhy8Zuj348f5L8eozT8kgcAAC/U++E4/pXCfy8OKpdrj9HJH8T/ejiOk5rgD4CbCf4A4Kms2vab0PrbKl+kP4tfz36x9PGFyOLXs1/EF+nP4geFP/+IMwMAALZZa5jEL+IwIn/44HG8mBCA26y7NRQAsKpJ22+V0C9ivvXH85pp+61i0vqLzJ6aAAAAADyevW38pWka79+/j88///y5pwLsgH/xb/2djY733a+9i//ur/3pO4/5D//+P4i/8YsPaz9Hazha+7Gzvv/3/jj+r//lP1z78f/cn/tT8d/6Uyd3HvPv/Rd/P/7Op/O1n2NRs1yKf/Pb/9idx/ysfRn/m/90hSU1l/Bb/8ifi2+9Ob7zmH/nP/1p/P32Dcu2ZFn8Tz79bvzZyEV+w5u4//DTWXz5X/zxGmPe7n/8j/+l+PrhwZ3H/Kt/+8cb++8wIuIbb17FX/tHfu3OY/4f//B9/Ed/vP5/rzf517/1j8ZxpXznMf/i3/o70R+PV277Tcy2/v6tv3sQ/8yf+fzRrxE3+d/91W/c+fmzwTD+tR/+ZKPP6Rox79ZrxJp+7fAg/uV//C/deYxrxMMse43YpKf4OeImrhFfcY14GNeIea4R63ONmOcasb5tvkas49/6u/95VAuFGz/nGjFvk9eI/ngzW6Xc9f27jWvEvJd0jfBzxPoe4xpx33lsi70O/jKvqgeW9J+12hsd7x99df++e+97g40/7zr+Ya8f/7DXX/vx/9RgeO8x/9Vlb6Pn+naJX5x6yXjjX9/2KLn3mL/fvrzxeUtZEofJ+ZqhX0REFtH7GHHDJu7tUbLxc+0l9/9C8/cuOvFxie//st5WK/ceczoYbvxckyV+XvjPWu2ILIt/efT7kcZ6SyakEfGbo9+Pv97+erzvDe49/jmuEUmWbfw5XSPm3XaNeEyuEQ+z9DVig7b15wjXiIdxjZjnGrE+14h5rhGPyzXiYZa5RqzjZ53urZ9zjZi36WvEJtz1/buNa8S8l3SN8HPE+nb1GrEJexv8Cf0A2CajXDH+hcpvRTNW+wH/6416/E9/4y9f3ai8uhb68XRKMY6T7GLtddLzEXGSXUQpNvMqUQAAAABYtLfBX6/Xe+4pAMCcD/mj+BD3v7JqVql0GPHqLz7SjFjFuuHtrPNcPUa5vf3xCwAAAIBn9qx/eTo7O4s0TTc+7ng8jvGG1lwGAJhYJ7wFANgHP211IiKiWS7GSa36zLMBAOA2z/6S8+Fwc2vxAqzrrzQPNzreSe3+tcFPapUHPW9rOHrQ3nwTf6pWjWZ5/eUjXy+xB8afOaht9Gu8zHxrxcLGv6+Hpfv/t/lrd2xAPUrTGK+4FPXrSvneP7IcloobP9da8f6Nxv/iUSPebXAz7T9zULv3mNeV8sbPtZjL3XvMX2keRn88XmsvhkVfb9Sf5BqxjmIut/HndI2Yd9c1Yh3LjOca8TDLXiM2yTVinmvE+lwj5rlGXP08+kfty7V2nf4f/q0fRUREKZ+LL7/zxbWfS10jHmbZa8QmfyZ1jZj3XNeIdXy9UY9q4eavo58j5m3yGrHJf3+3ff9u4+eIeS/pGrFNP0c8pn24RmyTXPaMm+ElSRIfPnx41Of42te+9qjjc7fhcBifPn2a3n7z5k2Uy/f/gwPu93//B+/jf/V3//MHj/M/+yf+cvy3//TJBmbEXd73+vGbv/+fRPKA/+sWcxH//j/1T3qF9TP5aasz/YPXQ/wf/uo34y81GxuYEQDAavw8s/t8D3eb799u8/0DnsuqOcuzNv6KxWKUy+UYDofRbDajsOIrHW4zHo+j0+lY7hOArfGzdvdBoV9ERJJdjSP4AwCe0/teP1rDZO3HWyoSAAAez7Mv9Xl0dBQfP36MiIhK5f6a6bJyuVycn59vbDw24/T0NHI3VJgPDg6i0fBKF1hFY4llHp5yHO7WGa3/x7HHGAcAYB3ve/34rT/4QYzS9V/RdNtSkQAA8JJ1Op24vLy8dv+qC3fmNzWhdZVKpSgUCtHpdDY6brHoD9nbKMuySNP02tszrjgLO2uZdaqfchwAAPZfa5g8KPSLiBil2YMagwAAsI82lZ9sRTpWr9ej3W7HYDDYWOtvU8uGslm5XO7Gxt9N9wEAPAZL1AEAAADbJpfLRT5/va+XZdlK4d9WBX/tdntjwV8+n496vb6Rsdic169f37npJADAY7JEHQAAsI5muRilfO7Bv0s0y1vxJ3lgCzUajRu3RBsOh/Hp06elx9mKq8wkpEuSzS710Ww2NzoewDbxAyc8rVE63qpxWM8ml6g7qW1oUgAAwNY7qVXjy+98YfUQYOttzV97hXQAq3vo/pj213w6F6PRVo3D6kr5zSwjvqlxAACA3eIFvLvvpFb1AkBg6/m/BMCOag2TSB6Y2yVZaK08kW6ymZbXpsYBAADgaWmMAfAUBH8AAAA8qfe9vj96AvAiaYwB8NgEfwAASzgdDDY4zvWNmgFeive9fvzWH/zgwcucffmdL4R/AAAAC/LPPYF19Hq9+MUvfvHc0wAAXpDOaDPLrG5qHIBd1RomDwr9IiJGafagxiAAAMC+2sngr9/vP/cUAAAAAAAAYKtsbKnPJEliNBpFlmVRKBSiVCpFPr/5XLHX6wn+AAAAAAAAYMGDg7/RaBRnZ2cxHl9ftqpSqcTBwUFUKpWHPk1ERFxcXMTl5eVGxgIAAACAXdIsF6OUzz14n9RmeWNdAABgyzzo//Kj0Sg+fvx46+cHg0EMBoM4ODiIo6OjBz3PbeEiAAAs63Qw3KpxAOApCY1230mtGl9+54sH7XPaLBfjpFbd4KwAgG3yoJ/UWq3WUsddXl7GeDyO4+PjlZ9Dyw+AfVAvbmb5602NAy9VZ7T+H8keYxwAeEpCo/1wUqvGSe25ZwEAbKu1g7/BYBCj0eja/YVCIQqFQozH47mGXr/fj4uLi6Wbf/e1/A4PD9ebOAA8gz9Vr2/VOAAAvExCIwCA/bZ28NftduduHx4eRqPRmLsvTdNp4JdlWVxeXkatVotSqXTn2He1/KrVajSbzcjnNR4A2B2vK+WtGgcAYB2WigQAgO229k/as028V69eRa12/eVi+Xw+6vV6VKvV+PTpUyRJEq1WK96+fXvjmHe1/AqFQjSbzahUKutOGQAAAHgAS0UCAMB2Wzv4S9M0IiJKpdKNod+sfD4f7969i/fv38doNIrBYHAtwLur5XdwcLD0EqEAsI1OB8OtGgcAYF2WigQAgO314MbfwcHB0o9pNptxenoa/X5/Gvzd1fIrlUrx6tWrKBYtAQLAbuuM1n9V/GOMw+ouRpsJXTc1DutplApbNQ4AAADAJj04UVsllKtUKlGtVmMwGETE3S2/m/YMBOArGmTwtC6G11+k9JzjsJ7XG1o2flPjAAAAAGzSg4O/QmG1VzvX6/U4PT2N9+/fa/kBPEi2ZeNwFy2j3VfM57ZqHHjJ3vf69hcDAACAG6ydruVyuciyLPL5/EqPmyzxeVPop+UHsDytld3i+wXb4fRXK09sZhw/tz6H971+/NYf/CBG6fovXCnlc/Hld74Q/gEAALB3HlyrS9N05fBvkZYfAABPoTPazFKrmxqH1bWGyYNCv4iIUZpFa5jESW1DkwIAAIAt8eCkbTwerxz8TdqCEVp+AMBuqBc3s8zqpsYB2FXNcjFK+dyDW5vNsheOAgAALFr7N6V8Ph/j8Tg6nU40Go0olUorPb5QKMTx8fHKjwMAeA5HG/qZZVPjAOyqk1o1vvzOF/ZpBAAAeAQPfolkv9+Pfr8fEVdLdi6+3aTZbEattv66OkmSRL/f1xT8lTRNo9PpRLfbjZOTkwcvvQoAAPCYTmpVS60CAAA8go2ujTIajWI0Gs3dl8vlroWBDwn9Js/TbrefNPgbDAbR7/djOBzGeDyOLMsil8tFoVCIWq0W9Xr9yQO3JEni8vIyut3u9L5N7LkYsZ3nCwAAAAAAwO0efVOELMtiOBzGcDicu79QKMyFgYVCIYrF5aYzHo8fY6o3Go1GcXZ2Nn3OYrEYpVIp0jSNJEkiSZJot9vRbrej2WxGvV5/kjl1Op1p03LTY2/b+QIAAAAAAHC/tYO/NE2nHxcKhUjTNLJs+c3Zx+NxjMfja+HVYhhYKpWuNcueKvjrdrvRarUiIqJer8fh4eHcXNI0jVarNT2HVqsVSZLE0dHRxueSpml0u93odruPdv7bdL4AAI+hUdrM6942NQ4AAADAJj34LxafffbZNBxK0zTG43GMRqNIkmS69OcqgeBdy4UWi8VI0/RRmm6LBoPBNAQ7ODi4MdzK5/NxfHwcZ2dn0zldXl5uZDnTiTRN4+zs7Fpjsl6vx8HBQXz8+HGlr+9ttuV8AQAe0+tKeavGAQAAANikBwV/i228fD4f+Xw+SqXS3HGzgeDkLUmSpQOr25YLfUxnZ2cRcRU63tdoazabc2Fkq9XaWBCWz+en553L5aLRaMztr5fP5zfSANyW8wUAAAAAAGA9awd/WZZFLpdb6ti7AsHZMHASDj63TqczDSUbjca9x+fz+Tg4OIjLy8uIuPradLvdje1/V61Wo1Kp3Djest+Du2zb+QIAPJbTwWZeSLapcQAAAAA26UGNv8W999Z5fKVSiUqlMnf/ZJnQ2ZbgU+3rF3EVhE1Uq9WlHlOr1aZBWEREr9fbWBB2fHy8kXFus23nCwDwWDqjZKvGAQAAANiktYO/r33ta5ucx5xisRjF4vWpjUaj6Ha70e12H+25e73etP2Wy+VunMdNFtuMT7ks6UO8tPOFfXI6GGxwnPvbvjxMs1yMUj4Xo/TmZa6/Of5Z/POj/zj+3dI/HT8qfP3GY0r5XDTLD96eFwAAAADYUzv118NSqRTNZjPSNJ3bY26TZgOsxXDrPsViMZLkq1d/DwaDa23GbfPSzhf2SWe0mSb0psbhbie1anz5nS+iNbyhJZRl8Wd/8H+J6vBT/BuV/yR+/sV/J+KGpZyb5WKc1JZrZrN5jVJhq8YBAAAAgEU7FfxNlEqlRwv+er3e9ONl228T5XJ5Lgh7yuVJ1/XSzhf2SaO0mUv4psbhfie1apzUbvjEL/92RPuPIiKi2v6j+EuD/yLis28/7eS419cPD+5sbS6jlM/F1w8PNjgrAAAAAPjKTv6196F7C94mTdPpspcRV0tfrqJQmH8F/2woto1e2vnCvnldKW/VOKwpyyJ+8v2IXD4iS6/e/+T7ESffurH1x/O5s7W5JK1NAAAAAB7TTgZ/5XI5Dg42/2r5xcbaqg24xSBsNBo9eE6P6aWdL8BWev/DiPOffnU7S69uv/+h1t8WurW1yc6wZOvuu2/P1GXYMxUAAIB9tZO/7RaLxTg6Otr4uIvB1aoNuMdqIj6Wl3a+AFtnse03ofUHcCvtWwAAALjdTgZ/jyVN0/sPusNiEPbQ8R7bSztfgK2z2Pab0PqDR9MZbWZP4k2Nw3q0bwEAAOBmgr8ZDw2uFhtzs/vnbaPnON9VlwMtFArXlhQF2Au3tf0mtP4AAAAAYO+Mx+NrW7HdZdVcRfA3YzG42velLJ/jfC8uLlY6vtFoxOHh4SPNBuAZ3db2m9D6AwAAAIC90+12o9PpPNr4+51sPdCmG3Hb7qWdL8CzmW373WXS+tvyBjkAAAAAsB0Efxu02Jjb98bgSztfgI2ZtP1uWuJz1mzrDwAAAADgHpb63KCHNuZ2zTrne3R0FKVSaenj7e8H7J379vZbZK8/AAAAANgb9Xo9KpXK0sePRqOVtlET/M1YXKpycQ+8VW17A+45zrdUKkW5XH7Q8wDstPv29ltkrz8AAAAA2BuFQuFRS0/bnUw9scXgatVG22Jwtkqz7Tm8tPMFeHbL7u23yF5/AAAAAMASBH8zFoOw8Xi80uMXg7NtX6bypZ0vwLNbdm+/Rfb6g41plDaz4MWmxgEAAADYJMHfjMXG2qoNuMXji8Xt/oPQSztfgGc1afvFuvv05bT+YANeVzaz5PimxgEAAADYJMHfjMXG2mg0Wunxi425bV/68qWdL+ybZrkYpfy6IdKVUj4XzbLQ/kmkSUTvQ0SsG9xlEb2PV+MAAAAAANzAX3tn5PP5yOVy073rVl36cvb4cnn7XwX+0s4X9s1JrRpffueLaA3XD4Ka5WKc1KobnBW3KpQivvu9iGFr/TEqr67GAQAAAAC4geBvQaVSiX6/P709Go2WbrINh8O5cXbBSztf2DcntWqc1J57Fizrfe4wWrn1v2HNKMbJBucDAAAAAOwXwd+CarU6F4QNBoOlg7Ak+ap1U6/X7zw2TdMYDAaRz+efNTR7qvMFeOne9/rxW3/wgxil6+/RV8rn4svvfKGlCQAAAADcyB5/C2q1+SZGt9td6nG9Xm/6cbVajXz+9i9tkiTx/v37OD8/j9PT0/j06dN6k42YLtO5rqc4XwAiWsPkQaFfRMQozR60tCsAAAAAsN+kNTc4PDycfjwej2MwGNz7mE6nc+Pjb9Jut+cCu+FwuHTgtihN07nb6wSBj32+AADbolkuRimfe9AYpXwummULZwAAAADbx18sbtBoNKLb7cZ4PI6IiFarFW/fvr211dbtdqfLXjabzSgW7/6yzi6Redd99xkMBteCvm63G81mc6VxHvt8AQC2xUmtGl9+54sHtWeb5aIldwEAAICtJLG5xdu3b+P9+/eRZVmMx+P4+PFjHB8fX9v/rtPpRLvdjoiIg4ODpfa6q9Vq08fM3neX0WgUg8Eg0jSNLMsiSZIYDofXjut2u9N9+gqFQuTz+SiVSvfuI/iY5wsAsE1OatU4ue1Hr/c/ivjD3434jd+JOPnmk84LAAAA4KEEf7fI5/NxcnISrVYr+v3+NAwrFApRKpUiTdMYjUbTxt2rV6/uDe8mGo1GpGka3W438vl8HB4eXgvYFg0Gg2th4W3G4/G0vRdxtQfffcHfY54vAMBOyLKIH38Z0f751ft334jIPWxZUAAAAICnJPi7Qz6fj+Pj4xiNRtMmXZqm0e/3p4FYrVZbq/V2dHQUR0dHSx/faDSi0Wis/DyreMzzBQDYeu9/GHH+06uPz396dfuzbz/vnAAAAABWIPhbQqlUWnnfvF320s4XACCyLOIn34/I5SOy9Or9T74fcfItrT8AAABgZ+SfewIAAPDsJm2/LL26naVftf4AAAAAdoTgDwCAl2227Tdr0vr71R7HAAAAANtO8AcAwMu22Pab0PoDAAAAdozgDwCAl+u2tt+E1h8AAACwQwR/AAC8XLe1/Sa0/gAAAIAdUnzuCQAA7Ir3vX60hsnaj2+Wi3FSq25wRjzIbNvvtuAv4qvW38m3InK5p5sfAAAAwIoEfwAAS3jf68dv/cEPYpSuv+RjKZ+LL7/zhfBvW0zafveZbf199u3HnxcAAADAmiz1CQCwhNYweVDoFxExSrMHNQbZoPv29ltkrz8AAABgBwj+AOAJNMvFKOUftkRgKZ+LZllZHzbivr39FtnrDwAAANgB/noIAE/gpFaNf/uf/CfiF93B2mN8rV6xRCRswrJ7+y2y1x8AAACw5QR/APAE3vf68a/8J3/X/nCwDZbd22+Rvf4AAACALWepTwB4AvaH23/fHP8s/s/9/1N8c/yz554Kd5m0/WLdxl7OXn8AAADA1hL8AQA8VJbFXxv9zfhz2af4a6O/KRTaZmkS0fsQEet+j7K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" ] @@ -557,20 +567,18 @@ " ax, n, l_tot, c_tot = [], 0, 2, 1\n", " n += 1\n", " ax.append(plt.subplot(l_tot, c_tot, n))\n", - " x = np.linspace(20, 7000)\n", - " ax[-1].semilogx(all_N, R10_inter_vs_N, \"s\", color=colors[\"mycyan\"],\n", + " x = np.linspace(200, 7000)\n", + " ax[-1].errorbar(unique_N, unique_mR1, unique_sR1, unique_sR1*0, \"s\", color=colors[\"mycyan\"],\n", " markersize = 12, linewidth=4, label=r'$R_1^\\mathrm{inter}$')\n", - " ax[-1].semilogx(x, x*0 + R10_inter_vs_N[-1],\n", - " \"--\", color=colors[\"mycyan\"],\n", - " markersize = 12, linewidth=4)\n", - " ax[-1].semilogx(all_N, R10_intra_vs_N, \"^\", color=colors[\"myorange\"],\n", - " markersize = 12, linewidth=4, label=r'$R_1^\\mathrm{intra}$')\n", - " ax[-1].semilogx(x, x*0 + R10_intra_vs_N[-1],\n", - " \"--\", color=colors[\"myorange\"],\n", - " markersize = 12, linewidth=4)\n", - " complete_panel(ax[-1], r'$N$', r'$R_1$ (s$^{-1}$)',\n", - " legend=True, axis_color=mygray, xpad=15)\n", - " set_boundaries(plt, x_boundaries=(10, 8000), y_boundaries=(0, 0.3))\n", + " ax[-1].semilogx(unique_N, unique_mR1, \"s\", color=colors[\"mycyan\"],\n", + " markersize = 12, linewidth=4, label=r'$R_1^\\mathrm{inter}$')\n", + " #x = np.linspace(20, 7000)\n", + " #ax[-1].semilogx(x, x*0 + R10_inter_vs_N[-1],\n", + " # \"--\", color=colors[\"mycyan\"],\n", + " # markersize = 12, linewidth=4)\n", + " complete_panel(ax[-1], r'Number of molecule $N$', r'$R_1^\\mathrm{inter}$ (s$^{-1}$)',\n", + " legend=False, axis_color=mygray, xpad=15, locator_x = None)\n", + " set_boundaries(plt, x_boundaries=(80, 20000), y_boundaries=(0.1, 0.14), y_ticks=np.arange(0.1, 0.142, 0.01))\n", " # x_ticks=np.arange(-1, 0.2, 0.2)\n", " # add_subplotlabels(fig, ax, [\"a\", \"b\"], color=mygray)\n", " save_figure(plt, fig, mode, git_path, path_figures, filename)"